similar to: Ordered Logistic Regression in survey command

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

Hi, I'm trying to fit a ordered logistic regression. The response variable (y) has three levels (0,1,2). The command I've used is: /ordlog<-polr(y~x1+x2+x3+x4, data=finalbase, subset=heard, weight=wt, na.action=na.omit) / (There are no NA's in y but there are NA's in X's) The error I'm getting is: /Warning messages: 1: non-integer #successes in a binomial glm! in:

Hi, I'm trying to fit a ordered logistic regression. The response variable (y) has three levels (0,1,2). The command I've used is: ordlog<-polr(y~x1+x2+x3+x4, data=finalbase, subset=heard, weight=wt, na.action=na.omit) (There are no NA's in y but there are NA's in X's) The error I'm getting is: Warning messages: 1: non-integer #successes in a binomial glm! in:

Hi All, I'm trying to modify some of the default graphic parameters in a conditional histogram. While I was able to change the default grey background to white, I couldn't change the axis.font or the xlab font. I used the following code: /histogram(~V751|V013+V025, finalbase, xlab="Heard of HIV/AIDS (No/Yes)", col=c("cyan","magenta"),

Hi, I'm trying to compute survey svytable statistic on subsets by using the svyby function. Here is the code: b<-svyby(~V024+V751, by=~V025, design=strat2, svytable, round=TRUE) The vars, V024, V751 and V025 are factors. The by var has 2 levels, and hence there will be two subsets. strat2 is created by the svydesign function. It's giving me the following error: >

I am making exploratory analyses on a complex survey data by using survey package. Could you help me how to see the goodness of fit for the model below? Should I use AIC, BIC, ROC, or what? What code would let me run a goodness of fit test for the model? Here are my codes: #incorporating design effects# > mydesign <- svydesign(id=~clust, strata=~strat, weights=~sweight, > data=mydata)

Greetings, I am using library(survey) to analyze some complex sample data. After fitting a model I tried to use termplot() with make.panel.svysmooth(), but I received an error (see below). Could someone help me interpret the error message so I can make the necessary corrections? The make.panel.svysmooth() function seems to work fine, and termplot() worked fine after I dropped the smoother.

Hello, Is there a function/package that can do multinomial logistic regression using survey weights, similar to "svymlogit" in Stata? It appears that only "svyglm" function (which does not allow multinomial response?) is available in the "survey" package. Thank you! Masha Kocherginsky

Howdy, Referencing the below exchange: https://stat.ethz.ch/pipermail/r-help/2006-April/103862.html I am still getting the same warning ("non-integer #successes in a binomial glm!") when using svyglm:::survey. Using the API data: library(survey) data(api) #stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

Hello, I want to specify a linear regression model in which the metric outcome is predicted by two factors and their interaction. glm() computes effects for each factor level and the levels of the interaction. In the case of singularities glm() displays "NA" for the corresponding coefficients. However, svyglm() aborts with an error message. Is there a possibility that svyglm()

I?m running some logistic regressions and I?ve been trying to include weights in the equation. However, when I run the model, I get this warning message: Here?s what it says: Warning message: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! I think it is because the weights are non-integer values. What is a good way to run logistic regressions in R when using

Dear all, I'm working on a design with rotating panels. Each site is sampled every X year. Have a look at the code below the get an idea of the design. In reality the number of sites will be (much) higher. However I'm not sure if I coded the design correctly in svydesign(). Can someone confirm that it is either correct or wrong? I've added the lmer() equivalent that I want to

Hello, I'm running R 2.14.1 on OS X (x86_64-apple-darwin9.8.0/x86_64 (64-bit)), with version 3.28 of Thomas Lumley's survey package. I was using predict() from svyglm(). E.g.: data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) out <- svyglm(sch.wide~ell+mobility, design=dstrat, family=quasibinomial()) pred.df <-

Hello R-list, I'm attempting to migrate from Stata to R for my complex survey work. It has been straight-forward so far except for the following problem: I have some code below, but first I'll describe the problem. When I compute predicted logits from a logistic regression, the standard errors of the predicted logits are way off (but the predicted logits are fine). Furthermore, the

I have been asked to look at options for doing relative risk regression on some survey data. I have a binary DV and several predictor / adjustment variables. In R, would this be as "simple" as using the survey package to set up an appropriate design object and then running svyglm with family=binomial(log) ? Any other suggestions for covariate adjustment of relative risk

Estimados usuarios: Estoy intentando reproducir el ejemplo 6.4 de Thomas Lumley. Complex Survey. Editorial Wiley. 2010 (ver la página en google:

Dear R-Users! Is there a possibility in R to do analyze longitudinal survey data (repeated measures in a survey)? I know that for longitudinal data I can use lme() to incorporate the correlation structure within individual and I know that there is the package survey for analyzing survey data. How can I combine both? I am trying to calculate design-based estimates. However, if I use svyglm() from

Using the survey package I find it is convenient and easy to get estimated proportions using svymean, and their corresponding estimated standard errors. But is there any elegant/simple way to calculate corresponding confidence intervals for those proportions? Of course +/- 1.96 s.e. is a reasonable approximation for a 95% CI, but (incorrectly) assumes symmetrical distribution for a proportion.

Hello, Seeking a way to get the variance inflation factor VIF for a model of regression in complex survey, I have understood that without this package (SURVEY) RGui VIF obtained as follows: fit <- lm(mpg~disp+hp+wt+drat, data=mtcars) vif(fit) But I want to know if survey, Vif is obtained so vif( svyglm(api00~ell+meals+mobility, design=dstrat)) Thank you, happy day

Dear list colleagues, I'm trying to come up with a test question for undergraduates to illustrate comparison of means from a complex survey design. The data for the example looks roughly like this: mytest<-data.frame(harper=rnorm(500, mean=60, sd=1), party=sample(c("BQ", "NDP", "Conservative", "Liberal", "None", NA), size=500,